FT-GAN: Fine-Grained Tune Modeling for Chinese Opera Synthesis

Abstract

Although singing voice synthesis (SVS) has made significant progress recently, with its unique styles and various genres, Chinese opera synthesis requires greater attention but is rarely studied for lack of training data and high expressiveness. In this work, we build a high-quality Gezi Opera (a type of Chinese opera popular in Fujian and Taiwan) audio-text alignment dataset and formulate specific data annotation methods applicable to Chinese operas. We propose FT-GAN, an acoustic model for fine-grained tune modeling in Chinese opera synthesis based on the empirical analysis of the differences between Chinese operas and pop songs. To further improve the quality of the synthesized opera, we propose a speech pre-training strategy for additional knowledge injection. The experimental results show that FT-GAN outperforms the strong baselines in SVS on the Gezi Opera synthesis task. Extensive experiments further verify that FT-GAN performs well on synthesis tasks of other operas such as Peking Opera. Audio samples, the dataset, and the codes are available at https://zhengmidon.github.io/FTGAN.github.io/.